scholarly journals Epilepsy Detection in EEG Using Grassmann Discriminant Analysis Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hongbin Yu ◽  
Chao Fan ◽  
Yunting Zhang
Keyword(s):  
Discriminant Analysis ◽  
Grassmann Manifold ◽  
Signal To Noise Ratio ◽  
Involuntary Movement ◽  
Experimental Scheme ◽  
Data Dimensionality Reduction ◽  
Eeg Data ◽  
Fréchet Mean ◽  
Benchmark Datasets ◽  
Frechet Mean

Epilepsy is marked by seizures stemming from abnormal electrical activity in the brain, causing involuntary movement or behavior. Many scientists have been working hard to explore the cause of epilepsy and seek the prevention and treatment. In the field of machine learning, epileptic diagnosis based on EEG signal has been a very hot research topic; many methods have been proposed, and considerable progress has been achieved. However, resorting the epileptic diagnosis techniques based on EEG to the reality applications still faces many challenges. Low signal-to-noise ratio (SNR) is one of the most important methodological challenges for EEG data collection and analysis. This paper discusses an automated diagnostic method for epileptic detection using a Fréchet Mean embedded in the Grassmann manifold analysis. Fréchet mean-based Grassmann discriminant analysis (FMGDA) algorithm to implement the EEG data dimensionality reduction and clustering task. The method is resorted to reduce Grassmann data from high-dimensional data to a relative lower-dimensional data and maximize between-class distance and minimize within-class distance simultaneously. Every EEG feature is mapped into the Grassmann manifold space first and then resort the Fréchet mean to represent the clustering center to carry out the clustering work. We designed a detailed experimental scheme to test the performance of our proposed algorithm; the test is assessed on several benchmark datasets. Experimental results have delivered that our approach leads to a significant improvement over state-of-the-art Grassmann manifold methods.

Fréchet mean-based Grassmann discriminant analysis

2019 ◽  
Vol 26 (1) ◽  
pp. 63-73
Author(s):  
Hongbin Yu ◽  
Kaijian Xia ◽  
Yizhang Jiang ◽  
Pengjiang Qian
Keyword(s):  
Discriminant Analysis ◽  
Fréchet Mean ◽  
Frechet Mean

scholarly journals An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3721 ◽  
Author(s):  
Usman Rashid ◽  
Imran Niazi ◽  
Nada Signal ◽  
Denise Taylor
Keyword(s):  
Experimental Study ◽  
Mixed Models ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Low Frequency ◽  
Low Power Consumption ◽  
Motor Tasks ◽  
Negative Peak ◽  
Eeg Data ◽  
Healthy Participants

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

scholarly journals BETTER ALTERNATIVES FOR STEPWISE DISCRIMINANT ANALYSIS

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Katarzyna Stąpor
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Tabu Search ◽  
Stepwise Discriminant Analysis ◽  
Selection Methods ◽  
Discrimination Power ◽  
Statistical Software ◽  
Software Packages ◽  
Benchmark Datasets

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.

Equivalence between LDA/QR and Direct LDA

2012 ◽  
pp. 338-353
Author(s):  
Rong-Hua Li ◽  
Shuang Liang ◽  
George Baciu ◽  
Eddie Chan
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Qr Decomposition ◽  
Linear Discriminant ◽  
Qr Algorithm ◽  
Special Cases ◽  
Benchmark Datasets ◽  
Pseudo Inverse

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They can be regarded as special cases of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA can also be considered as a two-stage LDA method. Interestingly, the first stage of DLDA can act as a dimension reduction algorithm. The experiment compares LDA/QR and DLDA algorithms in terms of classification accuracy, computational complexity on several benchmark datasets and compares their first stages. The results confirm the established equivalent relationship and verify their capabilities in dimension reduction.

scholarly journals Development of a Smart Helmet for Strategical BCI Applications

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1867 ◽  
Author(s):  
Li-Wei Ko ◽  
Yang Chang ◽  
Pei-Lun Wu ◽  
Heng-An Tzou ◽  
Sheng-Fu Chen ◽  
...  
Keyword(s):  
Data Collection ◽  
Signal To Noise Ratio ◽  
The Novel ◽  
Contact Impedance ◽  
Practical Applications ◽  
Dry Electrodes ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electrophysiological Measurements ◽  
User Acceptability

Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain–computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.

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